CN108776863A - One kind being based on the maximized intelligent perception motivational techniques of user base number - Google Patents

One kind being based on the maximized intelligent perception motivational techniques of user base number Download PDF

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CN108776863A
CN108776863A CN201810515776.5A CN201810515776A CN108776863A CN 108776863 A CN108776863 A CN 108776863A CN 201810515776 A CN201810515776 A CN 201810515776A CN 108776863 A CN108776863 A CN 108776863A
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user
perception
bid
task
aware services
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CN108776863B (en
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张幸林
李鑫
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South China University of Technology SCUT
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06311—Scheduling, planning or task assignment for a person or group
    • G06Q10/063112—Skill-based matching of a person or a group to a task
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06395—Quality analysis or management

Abstract

The invention discloses one kind being based on the maximized intelligent perception motivational techniques of user base number, includes the following steps：Perception task set is sent to aware services platform by S1, task requests end；Perception task set is sent to each user terminal in target apperception region by S2, aware services platform；S3, user terminal judge whether the perception task for itself meeting condition, if in the presence of the bid data of oneself is then submitted to aware services platform；S4, aware services platform receive the bid data of all user terminals, calculate most numbers that each perception task can recruit under budget limit based on excitation model, and perception task is distributed to corresponding acceptance of the bid user terminal；Sensing results that S5, aware services Platform integration receive simultaneously return result to task requests end, and payt is to acceptance of the bid user.The method can be under conditions of budgetary restraints, the area that user cost is unevenly distributed, and recruit more users and participate in perception task.

Description

One kind being based on the maximized intelligent perception motivational techniques of user base number
Technical field
The present invention relates to intelligent perception fields, and in particular to one kind being based on the maximized intelligent perception excitation side of user base number Method.
Background technology
In recent years, all kinds of emerging the applying in each side based on mobile intelligent perception (Mobile crowdsensing, MCS) Face affects people's lives.Mobile intelligent perception collects various information (such as positions, sound using normal smart phone user Sound, video, image etc.), it is convenient for people's lives to enable researcher to realize various Application in Sensing, as traffic monitoring, Pollution monitoring and social networks.In order to ensure these applications are capable of providing high-quality service, key factor is smart mobile phone The abundant participation of user.However, for MCS application programs, normal smart phone user may be made by executing perception task At various losses.For example, a large amount of battery capacity and additional data transmission cost may be consumed by completing sensing task.It receives The sensorial data of collection can also show the personal information of user.Therefore, it is necessary to provide a user enough excitations, user is made to be ready The perception resource for contributing them finally allows MCS application programs to provide the sensing service of high quality.
In newest research, many researchers do a lot of work, and devise various incentive mechanisms to encourage user It participates in, ensures that MCS application programs can provide the sensing service of high quality.And in these traditional methods, assume to use mostly Family is associated with the homogeneous cost of entire sensing region, and proposes a variety of Efficiency-optimized models on this basis.Design is based on The offer reward incentives mechanism of reverse auction is a up-and-coming method to encourage user to participate in.And the work on hand of MCS Assume that there are one global benefit functions to carry out Optimization Platform in sensing region mostly, the user that this optimization has ignored different zones can There can be the problem of heterogeneous cost.In this case, if using traditional mechanism, it is intended to recruit one group of user according to list Object function in terms of the contributrion margin of position, the user of recruitment may have the unbalanced distribution of height between different regions.At this A little areas lack the overall quality of service that the data collected limit MCS application programs, even if other regions can receive enough Data.
Therefore in view of smart phone user has heterogeneous cost in sensing region, for example, the user of different regions has not With cost distribution, traditional mechanism may will produce perception loophole, and the user recruited in certain areas be it is inadequate, It is undesirable so as to cause service quality.Under news, traditional method has been not suitable for, so being badly in need of designing new Incentive mechanism solves such case.
Invention content
It being based on the maximized gunz sense of user base number in view of the deficiencies of the prior art, it is an object of the present invention to provide a kind of Know that motivational techniques, the method propose a kind of incentive mechanism, can be distributed not in user cost under conditions of budgetary restraints Equal area recruits user as much as possible and participates in perception task, to improve the total quality of aware services.
The purpose of the present invention can be achieved through the following technical solutions：
One kind being based on the maximized intelligent perception motivational techniques of user base number, the described method comprises the following steps：
S1, task requests end will be sent to aware services by the service content asked and the perception task set that budget is constituted Platform waits for request to respond；
The perception task set received is sent in the user terminal set in target apperception region by S2, aware services platform Each user terminal；
S3, user terminal receive the perception task set of publication, judge in the perception task set with the presence or absence of certainly Body meets the perception task of condition, if in the presence of the bid data of oneself is then submitted to aware services platform；
S4, aware services platform receive the bid data of all user terminals, and each perception is calculated based on excitation model Most numbers that task can recruit under budget limit, and the perception task is distributed into corresponding acceptance of the bid user terminal；
The sensing results and inspection result that S5, aware services Platform integration receive, are then back to result and give task requests end, And payt is to acceptance of the bid user.
Further, consider that a perception task, the perception task include L interested sensing regions, first of perception Region and a series of candidate user RlIt is associated, wherein l=1,2 ... L, all candidate user setsAssuming that Perceived cost is distributed different interested sensing regions in heterogeneous, due to the perception matter of each interested sensing region Amount is assessed by participating user's quantity of aware services platform selecting, and the overall quality of perception task is used by minimum is received The limitation of the sensing region interested of amount, the object function design of aware services platform is as follows：
Wherein, | | indicate the user base number or quantity of selected user set,It is that first of sensing region corresponds to time Select family RlIn acceptance of the bid user set, piIndicate that the remuneration of acceptance of the bid user i, B indicate the total task budget of task publisher, [L] ={ 1,2 ..., L },Indicate selected user set.
Further, the object function of the aware services platform is based on dividing equally budget base maximization mechanism (Even- Budget Cardinality Maximization, EBCM) it is solved, detailed process is as follows：
1) budget is averagely allocated to each interested sensing region and corresponds to candidate user Rl；
2) candidate user R is corresponded to according to each interested sensing regionlThe n that bidslAscending order arrangement is carried out, in each sense Maximum k is selected in the sensing region of interestlA user so thatWhereinIndicate user kl∈RlBid；
3) quantity that the mechanism returns to covering collection is minl∈[L]kl, the remuneration threshold value of selected user is set as The user of selection is exactly final acceptance of the bid user.
Further, the object function of the aware services platform is based on minimum cardinality and maximizes mechanism (Min- Cardinality Maximization, MCM) it is solved, detailed process is as follows：
1) budget is averagely allocated to each interested sensing region and corresponds to candidate user Rl；
2) candidate user R is corresponded to according to each interested sensing regionlThe n that bidslAscending order arrangement is carried out, in each sense Maximum k is selected in the sensing region of interestlA user so thatWhereinIndicate user kl∈RlBid；
3) quantity that the mechanism returns to covering collection is minl∈[L]kl, the remuneration threshold value of selected user is set asThe user selected is potential acceptance of the bid user；
4) it subtracted the remuneration of recruitment of users and was added in the interested sensing region for owing recruitment, use is updated The interested sensing region of deficient recruitment recruit more users；
5) above step is repeated, until the number of users recruited in all interested sensing regions is suitable.
Compared with prior art, the present invention having the following advantages that and advantageous effect：
It is provided by the invention to be based on the maximized intelligent perception motivational techniques of user base number, it can there are different in sensing region In the case of matter cost, under conditions of ensureing budget limit, user as much as possible is recruited in each area-of-interest, from And improve the quality of aware services.
Description of the drawings
Fig. 1 is a kind of flow chart based on the maximized intelligent perception motivational techniques of user base number of the embodiment of the present invention.
Fig. 2 be the embodiment of the present invention under user cost normal distribution, tri- kinds of algorithms of MCM, EBCM and CGreedy are averaged Recruitment of users number performance compares figure.
Fig. 3 be the embodiment of the present invention under user cost normal distribution, the covering of tri- kinds of algorithms of MCM, EBCM and CGreedy Collection performance compares figure.
Fig. 4 be the embodiment of the present invention in the case where user cost is uniformly distributed, tri- kinds of algorithms of MCM, EBCM and CGreedy are averaged Recruitment of users number performance compares figure.
Fig. 5 be the embodiment of the present invention in the case where user cost is uniformly distributed, the covering of tri- kinds of algorithms of MCM, EBCM and CGreedy Collection performance compares figure.
Specific implementation mode
Present invention will now be described in further detail with reference to the embodiments and the accompanying drawings, but embodiments of the present invention are unlimited In this.
Embodiment：
It present embodiments provides a kind of based on the maximized intelligent perception motivational techniques of user base number, the flow of the method Figure is as shown in Figure 1, include the following steps：
S1, task requests end will be sent to aware services by the service content asked and the perception task set that budget is constituted Platform waits for request to respond；
The perception task set received is sent in the user terminal set in target apperception region by S2, aware services platform Each user terminal；
S3, user terminal receive the perception task set of publication, judge in the perception task set with the presence or absence of certainly Body meets the perception task of condition, if in the presence of the bid data of oneself is then submitted to aware services platform；
S4, aware services platform receive the bid data of all user terminals, and each perception is calculated based on excitation model Most numbers that task can recruit under budget limit, and the perception task is distributed into corresponding acceptance of the bid user terminal；
The sensing results and inspection result that S5, aware services Platform integration receive, are then back to result and give task requests end, And payt is to acceptance of the bid user.
Specifically, consider that a perception task, the perception task include L interested sensing regions, first of Perception Area Domain and a series of candidate user RlIt is associated, wherein l=1,2 ... L, all candidate user setsAssuming that sense Know that cost is distributed different interested sensing regions in heterogeneous, due to the perceived quality of each interested sensing region It is to be assessed by participating user's quantity of aware services platform selecting, the overall quality of perception task is by the minimum user of reception The limitation of several sensing regions interested, the object function design of aware services platform is as follows：
Wherein, | | indicate the user base number or quantity of selected user set,It is that first of sensing region corresponds to time Select family RlIn acceptance of the bid user set, piIndicate that the remuneration of acceptance of the bid user i, B indicate the total task budget of task publisher, [L] ={ 1,2 ..., L },Indicate selected user set.
Wherein, the object function of the aware services platform is based on dividing equally budget base maximization mechanism (Even-Budget Cardinality Maximization, EBCM) it is solved, detailed process is as follows：
1) budget is averagely allocated to each interested sensing region and corresponds to candidate user Rl；
2) candidate user R is corresponded to according to each interested sensing regionlThe n that bidslAscending order arrangement is carried out, in each sense Maximum k is selected in the sensing region of interestlA user so thatWhereinIndicate user kl∈RlBid；
3) quantity that the mechanism returns to covering collection is minl∈[L]kl, the remuneration threshold value of selected user is set asThe user of selection is exactly final acceptance of the bid user.
In addition, the object function of the aware services platform, which can also be based on minimum cardinality, maximizes mechanism (Min- Cardinality Maximization, MCM) it is solved, detailed process is as follows：
1) budget is averagely allocated to each interested sensing region and corresponds to candidate user Rl；
2) candidate user R is corresponded to according to each interested sensing regionlThe n that bidslAscending order arrangement is carried out, in each sense Maximum k is selected in the sensing region of interestlA user so thatWhereinIndicate user kl∈RlBid；
3) quantity that the mechanism returns to covering collection is minl∈[L]kl, the remuneration threshold value of selected user is set asThe user selected is potential acceptance of the bid user；
4) it subtracted the remuneration of recruitment of users and was added in the interested sensing region for owing recruitment, use is updated The interested sensing region of deficient recruitment recruit more users；
5) above step is repeated, until the number of users recruited in all interested sensing regions is suitable.
The design of used user selection and remuneration scheme in the algorithm, it is ensured that mechanism the result is that individual rationality and pre- Calculate feasibility.EBCM has polynomial time complexity from the point of view of instinctively.We illustrate that EBCM is in terms of following two Really：
In view of a series of bidding for budget and risings, payt scheme is true；
The bidding user of given specific ROI (interested sensing region, Region of Interest), remuneration only by The influence of user from identical ROI.
EBCM approximation ratio performances are summarized followed by following lemma：
Compared with best solution, the approximation ratio of EBCM is 2 for lemma 1
It proves：It is ranked up according to above-mentioned mechanism to bidding, then i-th of covering collection cviIt indicates, is owned by ith Minimum composition of bidding in ROI, i.e.,We use sumcviIndicate viIn it is all the sum of bid, can be with Readily appreciate that sumcviIt is also ascending order arrangement.Optimal solution (maximum quantity of the covering collection obtained) is the largest K*So that
Next we prove that the approximation ratio of EBCM is 2 with reduction to absurdity.
Assuming that the quantity of the covering collection returned is less than optimal several K*Half.It follows that Wherein
However, because of our ascending sorts to the progress of bidding of each ROI, all we have：
It was noted that
Then：
In conjunction with two above inequality, can draw a conclusionWith the hypothesis test of front.Card is finished.
We are with theorem 1 below it can be gathered that the property of EBCM
1 EBCM of theorem is computationally effective, is individual rational, feasible at last in advance, true and approximation ratio is 2。
In the present embodiment, the working method of the MCM mechanism is：
The main thought of MCM is to redistribute budget, the ROI that the ROI budgets for crossing recruitment are reallocated to deficient recruitment.
First, then our mean allocation budget B run MCM and obtain one group and potentially recruit user.Then the mistake subtracted The remuneration of recruitment of users is simultaneously added in the ROI for owing recruitment.We recruit more use with the ROI of updated deficient recruitment Family.Continuous service program, it is known that the number of users recruited in all ROI is suitable.
Next we analyze the property of MCM.It was noted that during budget is redistributed, we without departing from Budget B originally.Therefore MCM is that budget is feasible.Because MCM is attempted to improve by the quantity of the EBCM recruitment covering collection returned, Its approximation ratio upper limit is 2.
Other properties are obtained by several lemma.
2 MCM of lemma is computationally efficient.
It proves：Assuming that we have n user and L ROI, the time complexity that EBCM algorithms are spent is O (Ln2), sequence The time complexity of cost is O (L2).In while cycles, condition test quantity is limited by O (nL), most high in while cycles Expensive operation is search, its time complexity is O (L).Therefore, the total run time of MCM is O (Ln2) complexity.
3 MCM of lemma is individual rational.
It proves：For selected user, there are two types of situations.
Crossing the ROIR recruitedlIn.For a selected user i, remuneration is Wherein klIt is the user index that the last one obtained from EBCM is met the requirements.Therefore, Wo Menyou
In the ROIR for owing to recruitlIn.For a selected user i, remuneration isWherein klIt is The user index that the last one in while cycles is met the requirements.It is easy to see that the threshold value of this remuneration is not less thanAlways It, the user for participating in perception task will obtain non-negative benefit.
4 MCM of lemma is true.
It proves：There are two types of remuneration schemes in MCM.
Crossing the ROIR recruitedlIn.When selecting satisfactory user with EBCM, we have demonstrated that, remuneration Scheme is true.During budget is shifted, it is selected then list backend user be excluded last group acceptance of the bid use Except family.However, this operate does not interfere dull allocation rule, and the threshold value of remuneration and beginning property equally having the same Matter.Therefore, for crossing the user in the ROI recruited, which is true.
In the ROIR for owing to recruitlIn.According to MCM, budget the being to determine property of process reallocated.The mechanism most Afterwards, π is definedjFor sequence π1,π2,…,πLIn an element so thatAnd ROIBelong to the subset for owing recruitment. We only need to prove userDominant strategy can be reported strictly according to the facts.Assuming that user i has bona fide cost ciWith the b that bidsi.Therefore, True bid is best selection.
We are with theorem 2 below it can be gathered that the property of MCM
2 MCM of theorem is computationally effective, is individual rational, feasible at last in advance, true and approximation ratio is 2 's.
Fig. 4 and Fig. 5 illustrate user cost obedience be uniformly distributed and with different budgets when, the performance of algorithms of different. It can be seen that coverage rate and averagely recruitment number of users increase with the increase of given budget.
The covering collection number that Fig. 5 can be seen that MCM acquisitions is the largest.Specifically, the result average specific CGreedy of MCM 321%.The result shows that the mechanism can effectively utilize budget, improve overall quality of service, rather than as traditional mechanism that Sample only considers the most economical user in whole distract.The result average specific EBCM 15% of MCM, it means that budget transfer operation exists It is effective to make full use of budget front.
Fig. 4 compares the par for being recruited user of several algorithms, and the performance of three kinds of algorithms is suitable, wherein CGreedy is slightly better than other two kinds of algorithms.This show traditional mechanism CGreedy recruit user as much as possible be it is effective, But recruitment of users is in the unbalanced distribution of height in different ROI, some ROI receive seldom acceptance of the bid user so as to cause service Quality is low.
Fig. 2 and Fig. 3 depicts the performance trend when user cost Normal Distribution.From figure 3, it can be seen that MCM Covering integrates number averagely respectively than as CGreedy and EBCM high 124% and 28%, it can be seen that under the conditions of normal distribution, CGreedy reaches better as a result, this is because the user of low cost is fewer than equally distributed user, however, the mechanism also compares CGreedy is far better.
In the case of general, it can see from Fig. 2, average recruitment of users number MCM ratios CGreedy is slightly poor, so There is CGreedy the ability for selecting most economical user still from the perspective of overall quality of service, to find most economical use Family is clearly inadequate.
The above, patent preferred embodiment only of the present invention, but the protection domain of patent of the present invention is not limited to This, any one skilled in the art is in the range disclosed in patent of the present invention, according to the skill of patent of the present invention Art scheme and its patent of invention design are subject to equivalent substitution or change, belong to the protection domain of patent of the present invention.

Claims (4)

1. one kind being based on the maximized intelligent perception motivational techniques of user base number, which is characterized in that the method includes following steps Suddenly：
The perception task set being made of with budget the service content asked is sent to aware services and put down by S1, task requests end Platform waits for request to respond；
The perception task set received is sent to each in the user terminal set in target apperception region by S2, aware services platform A user terminal；
S3, user terminal receive the perception task set of publication, judge full with the presence or absence of itself in the perception task set The perception task of sufficient condition, if in the presence of the bid data of oneself is then submitted to aware services platform；
S4, aware services platform receive the bid data of all user terminals, and each perception task is calculated based on excitation model The most numbers that can be recruited under budget limit, and the perception task is distributed into corresponding acceptance of the bid user terminal；
The sensing results and inspection result that S5, aware services Platform integration receive, are then back to result and give task requests end, and prop up It pays to acceptance of the bid user.
2. according to claim 1 a kind of based on the maximized intelligent perception motivational techniques of user base number, it is characterised in that： Consider that a perception task, the perception task include L interested sensing regions, first of sensing region and a series of candidates User RlIt is associated, wherein l=1,2 ... L, all candidate user sets
Assuming that perceived cost is distributed different interested sensing regions in heterogeneous, due to each interested sensing region Perceived quality be to be assessed by participating user's quantity of aware services platform selecting, the overall quality of perception task is connect The limitation for receiving the sensing region interested of minimum number of users, the object function design of aware services platform is as follows：
Wherein, | | indicate the user base number or quantity of selected user set,It is that first of sensing region corresponds to candidate use Family RlIn acceptance of the bid user set, piThe remuneration of expression acceptance of the bid user i, the total task budget of B expression task publishers, [L]= { 1,2 ..., L },Indicate selected user set.
3. according to claim 2 a kind of based on the maximized intelligent perception motivational techniques of user base number, which is characterized in that The object function of the aware services platform is based on respectively budget base maximization mechanism and is solved, and detailed process is as follows：
1) budget is averagely allocated to each interested sensing region and corresponds to candidate user Rl；
2) candidate user R is corresponded to according to each interested sensing regionlThe n that bidslAscending order arrangement is carried out, each interested Sensing region in select maximum klA user so thatWhereinIndicate user kl∈RlBid；
3) quantity that the mechanism returns to covering collection is minl∈[L]kl, the remuneration threshold value of selected user is set as The user of selection is exactly final acceptance of the bid user.
4. according to claim 2 a kind of based on the maximized intelligent perception motivational techniques of user base number, which is characterized in that The object function of the aware services platform is based on minimum cardinality maximization mechanism and is solved, and detailed process is as follows：
1) budget is averagely allocated to each interested sensing region and corresponds to candidate user Rl；
2) candidate user R is corresponded to according to each interested sensing regionlThe n that bidslAscending order arrangement is carried out, each interested Sensing region in select maximum klA user so thatWhereinIndicate user kl∈RlBid；
3) quantity that the mechanism returns to covering collection is minl∈[L]kl, the remuneration threshold value of selected user is set as The user selected is potential acceptance of the bid user；
4) it subtracted the remuneration of recruitment of users and was added in the interested sensing region for owing recruitment, owed with updated Recruit more users in the interested sensing region recruited；
5) above step is repeated, until the number of users recruited in all interested sensing regions is suitable.
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